Critical States Preparation With Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework to efficiently prepare quantum critical states, overcoming traditional adiabatic limitations, demonstrated on the quantum Rabi model with high fidelity.

Contribution

It presents a novel DRL-based method for rapid quantum critical state preparation applicable to light-matter systems, surpassing existing techniques in speed and flexibility.

Findings

Achieved high-fidelity (>0.999) state preparation using DRL-optimized controls

Demonstrated the method on the quantum Rabi model with potential extension to other models

Provided a scalable framework for manipulating quantum critical states

Abstract

The fast and efficient preparation of quantum critical states is a challenging yet crucial task for various quantum technologies. This difficulty is most particularly for systems near a quantum phase transition, where the closure of the energy gap fundamentally limits the timescale of adiabatic processes and thus precludes rapid state preparation. We propose a framework using deep reinforcement learning (DRL) to rapidly prepare quantum critical states, with broad extendibility to light-matter interaction systems. Specifically, a DRL agent optimizes a set of time-dependent control Hamiltonians to drive the system from an initial noncritical state to a target critical state within a finite time and over experimentally accessible parameter ranges. As a concrete application, we focus on the quantum Rabi model. The DRL-optimized time-dependent control Hamiltonian yield a final state with high-fidelity ($>0.999$) to the target critical state. The protocol can be readily extended to other quantum critical systems described by light-matter interaction models, such as quantum Dicke model. This investigation provides a powerful new framework for preparing and manipulating quantum critical states.